Literature DB >> 16257080

CANSURV: A Windows program for population-based cancer survival analysis.

Binbing Yu1, Ram C Tiwari, Kathleen A Cronin, Chris McDonald, Eric J Feuer.   

Abstract

Patient survival is one of the most important measures of cancer patient care (the diagnosis and treatment of cancer). The optimal method for monitoring the progress of patient care across the full spectrum of provider settings is through the population-based study of cancer patient survival, which is only possible using data collected by population-based cancer registries. The probability of cure, "statistical cure", is defined for a cohort of cancer patients as the percent of patients whose annual death rate equals the death rate of general cancer-free population. Mixture cure models have been widely used to model failure time data. The models provide simultaneous estimates of the proportion of the patients cured from cancer and the distribution of the failure times for the uncured patients (latency distribution). CANSURV (CAN-cer SURVival) is a Windows software fitting both the standard survival models and the cure models to population-based cancer survival data. CANSURV can analyze both cause-specific survival data and, especially, relative survival data, which is the standard measure of net survival in population-based cancer studies. It can also fit parametric (cure) survival models to the individual data. The program is available at . The colorectal cancer survival data from the Surveillance, Epidemiology and End Results (SEER) program [Surveillance, Epidemiology and End Results Program, The Portable Survival System/Mainframe Survival System, National Cancer Institute, Bethesda, 1999.] of the National Cancer Institute, NIH is used to demonstrate the use of CANSURV program.

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Year:  2005        PMID: 16257080     DOI: 10.1016/j.cmpb.2005.08.002

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  7 in total

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Authors:  Angela B Mariotto; Ruth Etzioni; Marc Hurlbert; Lynne Penberthy; Musa Mayer
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2017-05-18       Impact factor: 4.254

2.  Current estimates of the cure fraction: a feasibility study of statistical cure for breast and colorectal cancer.

Authors:  Margaret R Stedman; Eric J Feuer; Angela B Mariotto
Journal:  J Natl Cancer Inst Monogr       Date:  2014-11

3.  Adolescent and young adult cancer survival.

Authors:  Denise Riedel Lewis; Nita L Seibel; Ashley Wilder Smith; Margaret R Stedman
Journal:  J Natl Cancer Inst Monogr       Date:  2014-11

4.  Can We Use Survival Data from Cancer Registries to Learn about Disease Recurrence? The Case of Breast Cancer.

Authors:  Angela B Mariotto; Zhaohui Zou; Fanni Zhang; Nadia Howlader; Allison W Kurian; Ruth Etzioni
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2018-10-18       Impact factor: 4.254

5.  Medical Care Costs Associated with Cancer Survivorship in the United States.

Authors:  Angela B Mariotto; Lindsey Enewold; Jingxuan Zhao; Christopher A Zeruto; K Robin Yabroff
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2020-06-10       Impact factor: 4.090

6.  A Survival Metadata Analysis Responsive Tool (SMART) for web-based analysis of patient survival and risk.

Authors:  Yuan-Chia Chu; Wen-Tsung Kuo; Yuan-Ren Cheng; Chung-Yuan Lee; Cheng-Ying Shiau; Der-Cherng Tarng; Feipei Lai
Journal:  Sci Rep       Date:  2018-08-27       Impact factor: 4.379

7.  REGSTATTOOLS: freeware statistical tools for the analysis of disease population databases used in health and social studies.

Authors:  Laura Esteban; Ramon Clèries; Jordi Gálvez; Laura Pareja; Josep Maria Escribà; Xavier Sanz; Angel Izquierdo; Jaume Galcerán; Josepa Ribes
Journal:  BMC Public Health       Date:  2013-03-07       Impact factor: 3.295

  7 in total

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